Continuous Y variable not displayed for all x values (ggplot) R

I am running the code below to visualize online social media sentiment per month for Feb through May, 2012. However, only data for Feb and March are displayed, although I have data for April and May as well.

Code for visualization:

valence_12<-valences_by_post %>%
  filter(year == 2012)%>%
  group_by(month) %>%
  summarize(mean_valence= mean(valence), n=n())

ggplot(valence_12, aes(x =month, y = mean_valence)) +
  geom_point() +
  geom_line()+
  scale_x_continuous(breaks=seq(1,5,1)) 
  geom_smooth(formula = y ~ x, method = "loess")

Output:

I am not sure why the mean for April-May is shown as NaN.

print(valence_12)
A tibble: 4 x 3
  month mean_valence     n
  <dbl>        <dbl> <int>
1     2       0.0514    35
2     3       0.0279   175
3     4     NaN        131
4     5     NaN         85

I am confused because when I ran the same code but visualizing sentiment by day for April, the graph displayed all as expected:

# Sentiment by day: April, 2012
valence_12<-valences_by_post %>%
  filter(month == 4)%>%
  group_by(day) %>%
  summarize(mean_valence= mean(valence), n=n())

ggplot(valence_12, aes(x =day, y = mean_valence)) +
  geom_point() +
  geom_line()+
  scale_x_continuous(breaks=seq(1,31,1)) +
  geom_smooth()

Output

How can I overcome the "NaN" error with the April and May data?

dput(valence_12)
structure(list(month = c(2, 3, 4, 5), mean_valence = c(0.0513884517137431, 
0.0279234111587779, NaN, NaN), n = c(35L, 175L, 131L, 85L)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -4L))

>Solution :

You have some missing values around April 23rd – hard to see exactly with your plot. You can interpolate those values, or if you are interested in summarizing by month just do na.rm = TRUE before creating your plot:

valence_12<-valences_by_post %>%
  filter(year == 2012)%>%
  group_by(month) %>%
  summarize(mean_valence= mean(valence, na.rm=TRUE), n=n())